Average behavior in learning models
研究一类适应性行为模型，其中遥远过去对当前行为影响微弱，且代理人偶尔犯错。结果表明，平均行为几乎必然收敛到唯一极限，错误作为均衡选择机制，在错误概率小时接近无错误模型的均衡。
We examine a general class of adaptive behavior models in which the distant past has only a weak effect on current actions, and assume that agents sometimes make mistakes, to show that average behavior (averaged over time) converges, with probability one, to a unique limit. Mistakes generate global convergence and are an equilibrium selection device; for small mistake probabilities the equilibrium selected is close to an equilibrium of the model without mistakes. The overlapping generations model, and learning in games with bounded memory, fit into this framework and are examined as examples of the result.